README.Rmd

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[Documentation](https://smilesun.github.io/rlR/)

# rlR: (Deep) Reinforcement learning in R

## Installation

### R package installation
```{r eval = FALSE}
devtools::install_github("smilesun/rlR")
```
or 

```{r eval = FALSE}
devtools::install_github("smilesun/rlR", dependencies = TRUE)
```

## Python dependency

rlR use keras with tensorflow as its backend for neural network as functional approximator and OpenAI gym.

see [Python Dependencies Installation and Configuration](https://smilesun.github.io/rlR/articles/python_dependencies.html)

## Example of Neural Network as Functional Approximator

### Choose an environment to learn
```{r}
library(rlR)
env = makeGymEnv("CartPole-v0")
env
```

If you have R package "imager" installed, you could get a snapshot of the environment by
```{r, eval=FALSE}
env$snapshot(preprocess = F)
```


### Initialize agent with the environment
```{r learn, eval=FALSE} 
agent = initAgent("AgentDQN", env)
agent$learn(200L)  
```

### Look at the performance
```{r mplot, eval=FALSE,fig.path="inst/figures/", warning=FALSE, message=FALSE, eval=FALSE}
agent$plotPerf(F)
```

## Specify a task to be sovled by creating your own Environment

see [Custom Environment](https://smilesun.github.io/rlR/articles/define_custom_environments.html)

## More Examples
- [Configuration](https://smilesun.github.io/rlR/articles/custom_configuration.html)
- [Tabular Learning](https://smilesun.github.io/rlR/articles/table_learning.html)
- [Repeated Experiment](https://smilesun.github.io/rlR/articles/repeated_experiment.html)
- Discover in [Documentation](https://smilesun.github.io/rlR/)